AI and technology · 1 min read
Being able to adjust the algorithm made it more popular, but slightly less accurate
When predicting students’ math performance, human oversight increased algorithm choice by 7 percentage points and slightly increased error.
The study, in plain language
What happens when someone can modify an automated recommendation before using it? In an experiment, participants predicted secondary students’ performance on a math test and compared algorithmic recommendations with human estimates.
Many people preferred the algorithm’s recommendation even though its average accuracy was similar to that of humans. With the option to review and adjust it, preference was even greater, but average accuracy fell slightly.
People intervened less on recommendations with larger errors and, when they did, corrected less of those errors. Having the final say did not guarantee a better prediction in this task.
The key idea
People preferred the algorithm even though it was about as accurate as a human estimate. Being able to adjust it increased preference and reduced accuracy.
Something to think about
If you have the final say on an automated recommendation, ask yourself what specific evidence would justify changing it.
The original study
Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making
Source under CC BY 4.0. English adaptation and explanation by PaperFlow, with AI assistance. The text has been reorganized for a general audience; it is not the complete paper and does not imply endorsement by its authors.
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